Methods: This cross-sectional study was conducted from October to December 2019 among 178 hospital workers at the Hospital Canselor Tuanku Muhriz in Kuala Lumpur, Malaysia. The study utilized a self-administered questionnaire that consisted of items on sociodemographics, work characteristics, sources of bullying, and the validated Malay version of the 23-item Negative Acts Questionnaire - revised to determine the prevalence of bullying. Descriptive and inferential statistics were analyzed using SPSS 22.0. Statistical significance was set at P<0.05.
Results: The prevalence of workplace bullying in this sample was 11.2%. Superiors or supervisors from other departments and colleagues were the main perpetrators. In the multivariate model, working for 10 years or less (aOR 4, 95% CI 1.3-12.3; P=0.014) and not being involved in patient care (aOR 5, 95% CI 2.5-10; P<0.001) were statistically significant attributes associated with workplace bullying.
Conclusion: Workplace bullying in the current study was strongly associated with occupational characteristics, particularly length of service and service orientation of the workers. Hospital directors and managers could undertake preventive measures to identify groups vulnerable to bullying and subsequently craft appropriate coping strategies and mentoring programs to curb bullying.
Method: The EEG signals are recorded for seven simple tasks using the designed data acquisition procedure. These seven tasks are conceivably used to control wheelchair movement and interact with others using any odd-ball paradigm. The proposed system records EEG signals from 10 individuals at eight-channel locations, during which the individual executes seven different mental tasks. The acquired brainwave patterns have been processed to eliminate noise, including artifacts and powerline noise, and are then partitioned into six different frequency bands. The proposed cross-correlation procedure then employs the segmented frequency bands from each channel to extract features. The cross-correlation procedure was used to obtain the coefficients in the frequency domain from consecutive frame samples. Then, the statistical measures ("minimum," "mean," "maximum," and "standard deviation") were derived from the cross-correlated signals. Finally, the extracted feature sets were validated through online sequential-extreme learning machine algorithm.
Results and Conclusion: The results of the classification networks were compared with each set of features, and the results indicated that μ (r) feature set based on cross-correlation signals had the best performance with a recognition rate of 91.93%.
Materials and Methods: In two tertiary care selected hospitals, the included diabetic patients were randomly divided into two study arms. In the control group, 200 patients who were receiving usual treatment from hospitals were included. However, in the intervention group, those 200 patients who were receiving usual treatment along with counseling sessions from pharmacists under the Diabetes Medication Therapy Adherence Clinic (DMTAC) program were included. The study continued for 1 year, and there were four follow-up visits for both study arms. A prevalidated data collection form was used to measure the improvement in predictors of diabetic foot in included patients. Data were analyzed by using the Statistical Package for the Social Sciences (SPSS) software program, version 24.0.
Results: With the average decrease of 1.97% of HbA1c values in the control group and 3.43% in the intervention group, the univariate and multivariate analysis showed a statistically significant difference between both of the study arms in the improvement of predictors belonging to the diabetic foot (P < 0.05). The proportion of patients without any signs and symptoms of the diabetic foot in the intervention group was 91.7%, which increased from 42.3% at baseline (P < 0.05). However, this proportion in the control group was 76.9% at the fourth follow-up, from 48.3% at baseline (P < 0.05).
Conclusion: A statistically significant reduction in the signs and symptoms of diabetic foot was observed in the intervention group at the end of 1 year. The progression of diabetic foot was significantly decreased in the pharmacist intervention group.
Materials and Methods: This is a single-center quasi-experimental study involving 100 patients seen in the outpatient department with knee osteoarthritis. They were randomly (computer generated) allocated into two arms (high frequency [H-F] or low frequency [L-F]). H-F is set at 100 Hz and L-F is set at 4 Hz. A baseline assessment is taken with the visual analog score (VAS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Oxford Knee Score, and Lequesne index. They were instructed to self-administer the TENS therapy as per protocol and followed up at the 4th and 12th week to be reevaluated on the above scores.
Results: The final results show that both H-F and L-F groups showed improvement in all parameters of the VAS, WOMAC index, Oxford Knee Score, and Lequesne index (73%). Only the pain component of Lequesne index, activities of daily living component of Lequesne index, total Lequesne index, and pain component of WOMAC index shows a statistically significant difference, favoring the H-F group. The H-F group yields a faster result; however, with time the overall effect remains the same in both groups.
Conclusion: Both H-F and L-F groups show improvement in all the component of Lequesne index, Oxford Knee Score, WOMAC index, and VAS with no statistical difference between the two groups. Although H-F yields a faster result, not everyone is able to tolerate the intensity. Therefore, the selection of H-F or L-F should be done on case basis depending on the severity of symptoms, patient's expectation, and patient's ability to withstand the treatment therapy. Based on this 12th week follow-up, both groups will continue to improve with time. A longer study should be conducted to see it this improvement will eventually plateau off or continue to improve until the patient is symptom free.